A scientometric study of three decades of machine translation research: Trending issues, hotspot research, and co-citation analysis

Abstract

This study aims to examine machine translation research in journals indexed in the Web of Science to find out the research trending issue, hotspot areas of research, and document co-citation analysis. To this end, 541 documents published between 1992 and 2022 were retrieved and analyzed using CiteSpace, and Bibexcel. Many metrics were analyzed such as document co-citation analysis, sources co-citation analyses, authors’ keywords analysis, and Hirsch index. Data were coded and filtered to include research related to machine translation from the perspectives of language and translation studies. We identified 11 clusters that represented the hotspot research during the period of almost three decades of research. We also discovered that a significant focus of research in machine translation centered around enhancing the translation process through the implementation of neural networks integrated with artificial intelligence. Additionally, we observed the incorporation of human post-editing as a means to refine and improve machine-translated outputs. We found that translation studies journals were the most highly co-cited journals and Google translate was the most highly used machine translation. This study highlights the trending issues and hotspots in machine translation research within language and translation studies. The integration of neural networks with artificial intelligence and human post-editing emerged as prominent areas of focus for enhancing translation quality. The findings of the current study inform future research and technological advancements in machine translation, guiding efforts to improve translation processes and outcomes.

Keywords:

• machine translation
• research
• scientometric
• translation studies
• co-citation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. We utilized a combination of Boolean operators (AND, OR) to employ various keywords in our search. The following was run the Web of Science (TS=(machine translation OR automated translation OR google translate or automatic translation)) AND ((SJ==(“LINGUISTICS” OR “LITERATURE” OR “ARTS HUMANITIES OTHER TOPICS” OR “EDUCATION EDUCATIONAL RESEARCH” OR “SOCIAL SCIENCES OTHER TOPICS” OR “SOCIAL ISSUES” OR “AUDIOLOGY SPEECH LANGUAGE PATHOLOGY”) AND DT==(“ARTICLE”) AND LA==(“ENGLISH”) AND DT==(“ARTICLE” OR “EARLY ACCESS” AND PY=(1981–2022).

Additional information

Funding

This study was funded by the Literature, Publishing and Translation Commission, Ministry of Culture, Kingdom of Saudi Arabia under [102/022] as part of the Arabic Observatorof Translation.

Notes on contributors

Mohammed Ali Mohsen

Mohammed Ali Mohsen is a professor of applied linguistics at the Department of English, Najran University, Saudi Arabia. His main research interests include CALL, vocabulary acquisition, L2 writing, cognitive processes, and scientometrics. His articles appeared in top-tier international journals including Computers & Education, Computer Assisted Language Learning, British Journal of Educational Technology, ReCALL, Language Teaching Research, Journal of Educational Computing Research, Interactive Learning Environments , Innovation in Language Learning and Teaching, and Journal of Computing in Higher Education.

Sultan Althebi

Sultan Althebi is a lecturer of applied linguistics at the Department of English, Najran University, Saudi Arabia. His main research interest include second language writing, CALL, and scientometrics.

Mohammed Albahooth

Mohammed Albahooth is a lecturer of translation studies at the Department of Translation, Najran University, Saudi Arabia. His main research interest include computer-assisted translation, translation studies, and audi-visual translation.